A simple agent-based model of a pandemic.
This is used to teach basic principles of object-oriented programming to master students.
It is not at all optimized for performance and does not have any predictive skill.
Based on an original idea by Georg Wohlfahrt.
We are simulating a square 2-dimensional (nx x nx pixel) domain which is populated
by a maximum number of agents (n_agents), i.e. human individuals. The initial position
of the agents on the domain is determined with a pseudo-random number generator.
Two or more agents may populate the same pixel.
The simulation runs to a maximum of nt time steps.
During every time step each agent may move a maximum number of pixels in
the horizontal (x) and vertical (y) direction, as determined by the
movemax parameter. If movemax = 2, this means that agents may move
between -2 and +2 pixels in the x/y-direction – the actual number of
moves within these limits is again determined with the random number generator.
If the random moves would cause an agent to leave the domain, the agent is
perfectly “reflected” into the domain, i.e. if a random move would bring an
agent to the position nx + 2, the position is corrected to nx – 2.
At the start of the simulation, a number n_initial_infected of
randomly selected agents carries the virus.
If two or more agents meet on the same pixel and at least one of them is
infected there is a certain probability (probability_of_infection)
that non-infected, non-immune agents become infected.
This dice roll is repeated for each infected agent at this location.
If infected, agents remain infectious until they recover (time_until_recovery)
unless they are quarantined, at the hospital, or deceased.
At the end of the infection period, there is also some probability that
agents do not recover, but instead go to the hospital (hospitalisation_rate).
After hospitalisation (time_hospitalised), patients either recover or
decease (mortality_rate), which reduces the total number of agents on the domain.
An infection conveys immunity from another infection. There is currently no vaccine for this decease.
There are many. See exercises on the topic.
Download the code and run pydemic.py in the terminal or in the ipython interpreter.
If you want a more interactive experience, you can also run the interactive_plots.ipynb
Jupyter notebook.
For the simulations:
- numpy
- xarray
- matplotlib
For the interactive plots:
- pandas
- bokeh
- holoviews
- hvplot
- Write tests.